Topics in Evolutionary Computation

Abstract

Autonomous robotic systems are expected to play a significant role in a wide range of areas including surveillance, deep space and undersea exploration and construction, urban search and recovery, mining, and hazardous waste cleanup. Systems that need to operate for extended periods of time out of range of human control should be adaptable to changing or unexpected conditions. This work examines some possible designs for such adaptive autonomous robotics systems, focusing on the adaptation to component failures in autonomous mobile robots. Adaptation is defined as the ability to continue to perform a task, perhaps at a degraded level, despite the loss of some of the robots original sensor and effector capabilities. The project addresses the problem of adaptation through an approach called Continuous Embedded Learning. Simulation and experimental results are reported.

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Document Details

Document Type
Technical Report
Publication Date
Jun 13, 2003
Accession Number
ADA417080

Entities

People

  • John J. Grefenstette

Organizations

  • George Mason University

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Systems
  • Case Studies
  • Climate Change
  • Computational Science
  • Computations
  • Deep Space
  • Environment
  • Evolutionary Algorithms
  • Failure Mode And Effect Analysis
  • Genetic Algorithms
  • Hazardous Waste
  • Learning
  • Robotics
  • Robots
  • Training

Fields of Study

  • Computer science

Readers

  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers